Ignite Creation Date:
2024-05-06 @ 7:31 PM
Last Modification Date:
2024-10-26 @ 3:08 PM
Study NCT ID:
NCT06031818
Status:
RECRUITING
Last Update Posted:
2024-02-06
First Post:
2023-09-04
Brief Title:
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework for Post-Hepatectomy Liver Failure Prediction
Sponsor:
Maastricht University
Organization:
Maastricht University
Study Overview
Official Title:
Usability and Clinical Effectiveness of an Interpretable Deep Learning Framework VAE-MILP Using Counterfactual Explanations and Layerwise Relevance Propagation Framework for Post-Hepatectomy Liver Failure Prediction
Status:
RECRUITING
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework VAE-MLP using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure PHLF in patients with hepatocellular carcinoma HCC The main questions it aims to answer are
To investigate the usability of the VAE-MLP framework for explanation of the deep learning model
To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation LRP plots to evaluate the usability of the framework
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework
To investigate the usability of the VAE-MLP framework for explanation of the deep learning model
To investigate the clinical effectiveness of VAE-MLP framework for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation LRP plots to evaluate the usability of the framework
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions with model explanation and without model explanation with a washout period of at least 14 days to evaluate the clinical effectiveness of the explanation framework
Detailed Description:
Post-hepatectomy liver failure PHLF is a severe complication after liver resection It is important to develop an interpretable model for predicting PHLF in order to facilitate effective collaboration with clinicians for decision-making Two-dimensional shear wave elastography 2D-SWE is a liver stiffness measurement LSM technology that was proven to be useful in liver fibrosis staging Therefore 2D-SWE shows the potential value for liver function assessment and PHLF prediction 2D-SWE images display color-coded tissue stiffness map of liver parenchyma with red representing a solid tissue higher stiffness and blue representing a soft tissue lower stiffness Routine analysis of 2D-SWE fails to fully utilize all information available in the images and also suffers from inter-observer variance in choosing the optimal quantification region
Deep learning DL has demonstrated state-of-the-art performance on many medical imaging tasks such as classification or segmentation However despite significant progress in DL the clinical translation of DL tools has so far been limited partially due to a lack of interpretability of models the so-called black box problem Interpretability of DL systems is important for fostering clinical trust as well as timely correcting any faulty processes in the algorithms
Here the investigators present a novel interpretable DL framework VAE-MLP which incorporates counterfactual analysis for the explanation of 2D medical images and LRP for the explanation of feature attributions of both medical images and clinical variables
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework VAE-MLP using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure PHLF in patients with hepatocellular carcinoma The main questions it aims to answer are
To investigate the usability of the the interpretable deep learning framework VAE-MLP for explanation of the deep learning model
To investigate the clinical effectiveness of the interpretable deep learning framework VAE-MLP for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation plots of 6 examples The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions with model explanation and without model explanation with a washout period of at least 14 days The accuracy sensitivity and specificity is used to compare the clinical effectiveness of the explanation framework
Deep learning DL has demonstrated state-of-the-art performance on many medical imaging tasks such as classification or segmentation However despite significant progress in DL the clinical translation of DL tools has so far been limited partially due to a lack of interpretability of models the so-called black box problem Interpretability of DL systems is important for fostering clinical trust as well as timely correcting any faulty processes in the algorithms
Here the investigators present a novel interpretable DL framework VAE-MLP which incorporates counterfactual analysis for the explanation of 2D medical images and LRP for the explanation of feature attributions of both medical images and clinical variables
The goal of this in-silico clinical trial is to learn about the usability and clinical effectiveness of an interpretable deep learning framework VAE-MLP using counterfactual explanations and layerwise relevance propagation for prediction of post-hepatectomy liver failure PHLF in patients with hepatocellular carcinoma The main questions it aims to answer are
To investigate the usability of the the interpretable deep learning framework VAE-MLP for explanation of the deep learning model
To investigate the clinical effectiveness of the interpretable deep learning framework VAE-MLP for prediction of post-hepatectomy liver failure in patients with hepatocellular carcinoma
In the usability trial the clinicians and radiologists will be shown the counterfactual explanations and layerwise relevance propagation plots of 6 examples The score of the Likert scale of a designed questionnaire is used to evaluate the usability of the framework
In the clinical trial the clinicians and radiologists will make the prediction under two different conditions with model explanation and without model explanation with a washout period of at least 14 days The accuracy sensitivity and specificity is used to compare the clinical effectiveness of the explanation framework
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None
Secondary IDs
| Secondary ID | Type | Domain | Link |
|---|---|---|---|
| 92059201 | OTHER_GRANT | National Natural Science Foundation of China | None |